U.S. patent number 5,999,854 [Application Number 09/059,860] was granted by the patent office on 1999-12-07 for implantable cardiac stimulator with physiologic sensor based on mechanical-electric phase relation.
This patent grant is currently assigned to Intermedics Inc.. Invention is credited to D. Curtis Deno, Nicholas F. Pergola, Daniel I. Sterling, Alec Vautravers.
United States Patent |
5,999,854 |
Deno , et al. |
December 7, 1999 |
**Please see images for:
( Certificate of Correction ) ** |
Implantable cardiac stimulator with physiologic sensor based on
mechanical-electric phase relation
Abstract
A cardiac pacemaker includes circuitry which receives a raw
impedance signal from the sensor leads of the pacemaker, derives
data from the impedance signal that is descriptive of the impedance
signal over an entire (or a large part of the) cardiac cycle,
develops first order parameters which define that cycle, and
provides these parameters to a microprocessor for control of the
pacing signal. These parameters may also be used to determine other
information about the functioning of the pacemaker. The present
invention may also be applied to the determination of tachycardia
of an intrinsically paced heart, as well as other applications.
Inventors: |
Deno; D. Curtis (Missouri City,
TX), Vautravers; Alec (Houston, TX), Pergola; Nicholas
F. (Arvada, TX), Sterling; Daniel I. (Houston, TX) |
Assignee: |
Intermedics Inc. (Angleton,
TX)
|
Family
ID: |
22025755 |
Appl.
No.: |
09/059,860 |
Filed: |
April 14, 1998 |
Current U.S.
Class: |
607/18 |
Current CPC
Class: |
G06K
9/00496 (20130101); A61N 1/36521 (20130101); A61N
1/365 (20130101) |
Current International
Class: |
A61N
1/365 (20060101); A61N 001/365 () |
Field of
Search: |
;607/17,18,24 |
References Cited
[Referenced By]
U.S. Patent Documents
Primary Examiner: Kamm; William E.
Attorney, Agent or Firm: Schwegman, Lundberg, Woessner &
Kluth, P.A.
Claims
We claim:
1. A method of controlling the pacing rate of a cardiac pacemaker
comprising the steps of:
a. receiving a sensor signal waveform from a sensor lead of a
pacemaker,
b. deriving data from the sensor signal waveform, the derived data
descriptive of the sensor signal waveform over at least most of a
cardiac cycle,
c. developing a first order parameter of the sensor signal waveform
which defines that cardiac cycle, and
d. providing the first order parameter to a microprocessor for
control of a pacing signal of the cardiac pacemaker.
2. The method of claim 1, wherein the sensor is an impedance
sensor.
3. The method of claim 1 further comprising the step of digitizing
the sensor signal waveform prior to the step of deriving data from
the sensor signal waveform.
4. The method of claim 1 wherein the first order parameter of the
sensor signal waveform comprises the phase of the sensor signal
waveform relative to a pacemaker or electrogram clock.
5. The method of claim 1 wherein the first order parameter of the
sensor signal waveform comprises the mean of the sensor signal
waveform.
6. The method of claim 1 wherein the first order parameter of the
sensor signal waveform comprises the magnitude of the sensor signal
waveform.
7. The method of claim 1 wherein the first order parameter of the
sensor signal waveform comprises the period of the sensor signal
waveform.
8. The method of claim 1 wherein the first order parameter of the
sensor signal waveform comprises a time parameter of the sensor
signal waveform relative to a pacemaker or electrogram clock.
9. The method of claim 1 wherein the step of developing a first
order parameter of the sensor signal waveform comprises developing
a composite first order parameter of the sensor signal waveform
from a function of the mean, magnitude, phase, period, and time
parameters of the sensor signal waveform.
10. The method of claim 1 wherein the step of deriving data from
the sensor signal waveform further comprises the step of
decomposing the sensor signal waveform into sine and cosine basis
function components.
11. The method of claim 1 wherein the step of deriving data from
the sensor signal waveform further comprises the step of developing
a piecewise constant approximation of the sine and cosine
components of the sensor signal waveform.
12. A device for controlling the pacing rate of a cardiac pacemaker
comprising:
a. means for receiving a sensor signal waveform from a sensor lead
of a pacemaker,
b. means for deriving data from the sensor signal waveform, the
derived data descriptive of the sensor signal waveform over at
least most of a cardiac cycle,
c. means for developing a first order parameter of the sensor
signal waveform which defines that cardiac cycle, and
d. means for providing the first order parameter to a
microprocessor for control of a pacing signal of the cardiac
pacemaker.
13. The device of claim 12, further comprising means for digitizing
the sensor signal waveform.
14. The device of claim 12, further comprising means for
decomposing the sensor signal waveform into basis functions, the
means for decomposing providing basis functions to the means for
developing a first order parameter.
15. The device of claim 12, further comprising means for
decomposing the sensor signal waveform into sine and cosine basis
functions, the means for decomposing providing basis functions to
the means for developing a first order parameter.
16. The device of claim 12, further comprising means for
decomposing the sensor signal waveform into sine-square and
cosine-square basis functions, the means for decomposing providing
basis functions to the means for developing a first order
parameter.
17. The device of claim 12, further comprising means for developing
a piecewise constant approximation of the sine and cosine
components of the sensor signal waveform, the means for developing
a piecewise constant approximation providing values to the means
for developing a first order parameter.
18. A cardiac stimulation apparatus comprising:
a. a stimulus generator for stimulating a patient's heart;
b. a sensor adapted to be coupled to the patient's heart for
sensing a time varying physiologic characteristic of the heart,
c. a signal injector for impressing a signal, on the heart which
develops the signal detected by the sensor;
d. an extractor coupled to the signal injector, the extractor
generating a first order parameter of the time varying
characteristic of the heart based upon at least most of a cardiac
cycle, and
e. a microprocessor to receive the first order parameter and
activate a pacing pulse from the stimulus generator based on the
first order parameter.
19. A method of controlling the pacing rate of a cardiac pacemaker
comprising the steps of:
a. sensing an impedance of a region of a heart with a sensor lead
of a pacemaker, resulting in an impedance signal,
b. sampling the impedance signal, resulting in a digitized
impedance signal,
c. blanking and padding the digitized impedance signal, resulting
in a filtered impedance signal,
d. synchronous averaging the filtered impedance signal, resulting
in a synchronous averaged impedance signal,
e. extracting a first order parameter from the synchronous averaged
impedance signal,
f. providing the first order parameter to a microprocessor for
control of a pacing signal of the cardiac pacemaker.
20. The method of claim 19, wherein the step of extracting a first
order parameter includes the steps of:
a. providing the synchronous averaged impedance signal and an event
trigger signal to a processor,
b. continuously updating a first set of registers in the processor,
the first set of registers storing a set of values representative
of the synchronous averaged impedance signal,
c. once each cardiac cycle, outputting the values in the first set
of registers to a second set of registers, the second set of
registers storing values representative of substantially all of a
cardiac cycle, and
d. calculating a first order parameter from the values stored in
the second set of registers.
21. The method of claim 20, wherein the first order parameter
comprises the phase of the impedance signal.
22. The method of claim 21, further comprising the step of storing
in the microprocessor a predetermined phase for comparison with the
phase of the impedance signal.
23. The method of claim 22, further comprising the step of
comparing the predetermined phase with the phase of the impedance
signal and thereby determining the pacing rate of the cardiac
pacemaker.
24. The method of claim 22, further comprising the step of
comparing the predetermined phase with the phase of the impedance
signal and thereby limiting the pacing rate of the cardiac
pacemaker.
25. The method of claim 22, further comprising the step of
comparing the predetermined phase with the phase of the impedance
signal and thereby initiating antitachycardia therapies including
burst pacing or defibrillation shocks.
26. The method of claim 22, further comprising the step of
comparing the predetermined phase with the phase of the impedance
signal while pacing at a rate or timing distinct from the safety
pulse or intrinsic rate to assess pacing capture and adjust pacing
output pulse amplitude accordingly.
27. A method of controlling the pacing rate of a cardiac pacemaker
comprising the steps of:
a. sensing an impedance of a region of a heart with a sensor lead
of a pacemaker, resulting in an impedance signal,
b. sampling the impedance signal, resulting in a digitized
impedance signal,
c. blanking and padding the digitized impedance signal, resulting
in a filtered impedance signal,
d. synchronous averaging the filtered impedance signal, resulting
in a synchronous averaged impedance signal,
e. extracting a harmonic of a first order parameter from the
synchronous averaged impedance signal,
f. providing the harmonic of the first order parameter to a
microprocessor for control of a pacing signal of the cardiac
pacemaker.
Description
FIELD OF THE INVENTION
The present invention relates generally to the field of cardiac
rhythm management devices and, more particularly, to a method and a
device for cardiac stimulation based on a measured relationship
between certain artificial stimuli and the heart's response to
those stimuli, the relationship referred to in this disclosure as
mechanical-electrical phase (MEP). This invention further relates
particularly to a method and an apparatus which derive
characteristics of an impedance signal over an entire cardiac cycle
to determine pacing and proper pacemaker function.
BACKGROUND OF THE INVENTION
A variety of cardiac pacemakers have been developed which rely upon
measured parameters to control heart rate in order to respond to
the level of activity of the patient. A number of these pacemakers
seek to eliminate or at least reduce the effects of extraneous or
otherwise interfering signals from the desired measured parameter.
For example, Deno, U.S. Pat. No. 5,507,785, discloses a rate
responsive pacemaker that is sensitive to impedance changes in the
heart as an indicator of cardiac stroke volume or minute volume.
This pacemaker uses a biphasic test signal to reduce or eliminate
common interfering signals from the measurement of the impedance.
This pacemaker also includes separate detector and injector
circuits so that a variety of electrode configurations may be
used.
Other proposed pacemakers are directed to certain measurements that
more precisely time the events of interest in the cardiac cycle.
U.S. Pat. No. 5,235,976 to Spinelli describes a parameter derived
from intracardiac impedance referred to as "total active time". The
active time is evaluated using the intraventricular impedance
technique, the active time being the length of the interval between
the onset of contraction and the point where a line passing through
two points on the fast filling segment of the impedance waveform
reaches the impedance level corresponding to the end-diastole
impedance of the preceding beat. In other words, the impedance
signal from the first part of the cardiac cycle is used to derive a
minimum (pacing) interval which just accommodates systolic ejection
and enough diastolic filling time to support adequate cardiac pump
function.
Unfortunately, this technique depends on local (in time) impedance
signal characteristics so that additional humps and variations in
morphology yields an estimate of total active time that is
unreliable. Furthermore, the total active time as determined by
Spinelli is not valid for the many cases at high pacing rate when
impedance peaks occur after the subsequent pace event.
Spinelli and other techniques also require a high sampling rate to
accurately determine crossing times on the impedance waveform. Such
a high sampling rate is very demanding of the power source for the
pacemaker and therefor reduces the length of time that the
pacemaker's installed power source may effectively perform its
intended functions. Thus, there remains a need for a cardiac
pacemaker that effectively controls cardiac function over a range
of demands but requires a much lower rate of sampling the impedance
signal over an entire cardiac cycle.
Other proposed solutions are directed to impedance signal
processing and certain physiologic sensor implementations. An early
example of such a pacemaker is provided in U.S. Pat. No. 4,773,401
to Citak et al. Citak et al. describe a method to determine the
pre-ejection period, or PEP, and use this parameter to control
pacing. With a few noteworthy exceptions, the resulting parameters
of such techniques have not been sufficiently reliable and robust
for commercial implementations.
Several factors make the physiologic sensing of heart function by
an implantable medical device difficult and thus yield less than
robust results. As a result of tradeoffs between size, weight,
longevity, and power, as well as mechanical and materials
compatibility, the resulting signals reflective of heart function
are often contaminated. Artifacts, noise, and variations from one
sensor to another and from one subject to another must be artfully
dealt with in robust, practical implementations.
One important example of a challenge to the art in sensing heart
function is the physiologic determination of maximum pacing rate in
response to an activity sensor or paroxysmal atrial tachycardia.
Other examples of the difficulties of sensing heart function
include tachycardia discrimination and hemodynamic tolerance
assessment for ventricular tachycardias and supra-ventricular
tachycardias (SVTs), as well as pacing and anti-tachycardia pacing
(ATP) capture detection for autothreshold and therapy termination
and success evaluation.
Thus, there remains a need for a rate responsive cardiac pacemaker
that is more immune to aberrations in sensor output waveforms,
including artifacts, noise, and variations from one sensor to
another. Such a pacemaker should be robust, should provide robust
and reliable responses to the impedance waveform, and should be
capable of practical implementation. A sensor must be combined with
good signal processing and parameter extraction to assist the
medical device to select appropriate therapeutic stimulation. Such
a sensor should also be capable of a full range of other
therapeutic and analytical functions.
SUMMARY OF THE INVENTION
The present invention addresses these and other drawbacks in the
art. A complete understanding of this invention begins with the
recognition that the impedance waveform of the cardiac cycle is
roughly sinusoidal and that any sinusoidal waveform may be
described by its first order parameters of amplitude, frequency,
and phase. The second factor in the understanding of this invention
is the recognition that changing physiologic or metabolic demand
under fixed heart rate and changes of heart rate under fixed demand
create changes in the first order parameters, particularly the
phase, and thus may signal a need for an alteration in cardiac
pacing.
Thus, in the broadest sense, the present invention receives a
sensor signal, in a preferred embodiment a raw impedance signal
from the sensor leads of a pacemaker, derives data descriptive of
the impedance signal over an entire cardiac cycle, develops first
order parameters which define that cycle, and provides these
parameters to a microprocessor for control of the electrical
therapy. These parameters may also be used to determine other
intelligence regarding the function of the pacemaker.
It will also be understood by those skilled in the pacemaker and
related arts that other physiologic parameters which may be used to
develop sensor signals may be effectively used, such as cardiac
wall tension or ventricular blood pressure, or other heart motion
parameters. As used in this description, impedance signal is used
throughout for consistency.
In another aspect, this invention provides a device and a method
for robustly deriving physiologic information from implanted pacing
lead impedance or other sensor signals. The present invention also
provides a broader view of impedance or other sensor applications
and signal processing elements desirable in an integrated circuit
implementation for future electrogram and other sensor signals.
This invention also supplies a robust amplitude measure which, in
addition to being useful directly, may also be used to quantify the
confidence of the mechanical-electric phase and time
parameters.
In yet another aspect of this invention, a sensor sampling and
processing system samples a plurality of physiologic parameters of
interest over a cardiac cycle by a variety of methods. This related
set of parameter extraction methods utilizes a new
paradigm--Fourier-like attributes of a sensor signal over a whole
(or almost all of) cardiac cycle. This extends parameter extraction
in cases like pacing lead impedance in which the sensor signal is
insufficiently reliable to utilize either the time at which it (or
processed versions of it such as its derivative) meets a threshold
criterion or its value at special points in time, such as the
minimum or maximum over a cardiac cycle.
The mechanical-electric phase (MEP-phase or simply PHA), time
(MEP-time or TIM), and magnitude (MEP-magnitude or MAG)
applications of this invention are exceptionally robust statistics
which are derived from the impedance waveform over the whole (or
large part) of the cardiac cycle. The information extracted is
thereby not dependent on detailed wave shape, but rather the
computation is spread in time over the cardiac cycle. As a result,
noise and moderate sized artifacts such as multiple maxima or
minima or ripples during signal rises and falls do not
significantly disturb PHA, TIM, or MAG, whereas they can send the
pre-ejection period (PEP) and other parameters known in the art to
wildly variable values. The process for parameter extraction from
sensor signals constitutes a departure from known systems which
rely on an impedance signal at a point in time (or, for example,
its filtered derivative computed over a narrow time window).
Rather, this invention relies on the majority of the signal over a
cardiac cycle.
Even when an impedance signal is heavily filtered in order to deal
with the uncertainty created by multiple extrema or ripples,
conventional point extraction techniques give rise to considerable
temporal uncertainty. This problem is fundamental to the
determination of extrema because the waveform is flat near a local
extremum. To a lesser, but important extent, there remains
uncertainty even with threshold crossing time due to waveform
variations.
Another important advantage of the MEP parameter PHA is that it is
self referenced and that a fixed upper parameter limit may be
predetermined independent of subject-to-subject variation. For
instance, an upper rate limit might be defined to occur when PHA
indicates the peak of the impedance occurs more than 65% into the
cardiac cycle.
MEP-phase, MEP-time, and MEP-magnitude may be thought of as "first
order physiologic" parameters. Particularly at medium and high
cardiac rates, the intracardiac impedance signal resembles a
(noisy) sinusoid. Deviations from an idealized sinusoid are not
robust within or across subjects, particularly as rate and postures
change. The most fundamental first order information is the
frequency, phase, and amplitude of the sinusoid, beyond the
zero.sup.th order information of mean impedance, which is also a
byproduct of the MEP procedure of this invention. Frequency is most
precisely known from electrogram timing. Phase and amplitude are
contained in PHA and MAG, respectively, and TIM is derived from the
signal frequency and PHA. PHA is also considered first order in
that it may change by 50% of its total range (e.g.
100.degree.-280.degree.) as pacing rates vary from 70 to 140 bpm,
whereas PEP may change by 10% or less as rates vary from 70-140
bpm.
Because of the temporally delocalized computation, less low pass
filtering of the impedance signal is required. Indeed, little or no
filtering is required in most cases. Where lead impedance signals
appear variable from cycle to cycle, synchronous averaging (which
"smartly" applies FIR or IIR averaging to cardiac cycles of various
durations) as disclosed herein provides a resulting signal that is
highly usable and fully compatible with the MEP process.
Consequences of less filtering include less time delay introduced
by causal filters, more prompt parameter estimation, and more rapid
response to physiologic changes in the patient. Also, without any
other filtering, it recovers from "odd cycle behavior" events like
PVCs or posture change induced sudden shifts of impedance by the
next one or two beats. This rapid response facilitates outlier
removal schemes.
The equivalent of very fine temporal resolution from a high
sampling rate is achieved using the MEP method with a reduced
sampling frequency. Because of the integrated nature of the
calculations, the MEP method distinguishes shifts in MEP-phase and
MEP-time beyond the temporal resolution of the sampling rate. This
permits less electrical current consumption associated with
measurements and processing. In addition, relatively crude 4- to
8-bit AND conversions of impedance have proven sufficient if the
baseline offset impedance has been removed.
The MEP method implementations described here are computed in real
time from a few, simple logic and arithmetic operations. Such
implementations are compatible with low power CMOS IC design from a
state machine with a simple register-level interface.
Finally, MEP-magnitude provides a built-in confidence estimate for
PHA and TIM. A magnitude value near the sensor's noise floor
implies that phase or phase-derived time variables are not
accurately estimated. This permits Kalman- or Bayesian-like optimal
control implementations which gracefully deteriorate under noisy or
low amplitude waveforms as well as less sophisticated Trust-Don't
Trust decisions which factor into pacing rate limits or a decision
to defibrillate now versus later.
Also, this method of parameter extraction is not restricted to
intracardiac impedance but is more broadly applicable to any sensor
signal for which first order signal characteristics like the
fundamental frequency's amplitude and phase convey more reliable
information than classical amplitude or time based parameter
estimates.
In another aspect, this disclosure demonstrates the value of this
new way to derive physiologic information from sensor signals and
applications of these parameters. Important contributions and
distinctions from prior impedance signal art include:
(a) a meaningful, inherent signal-to-noise ratio evaluation for
confidence in physiologic parameter estimates;
(b) extracted parameters which are tolerant of non-physiologic
posture and motion artifacts;
(c) a method which is tolerant of reduced sensor sampling rate;
(d) a method which is tolerant of low signal amplitude
resolution;
(e) a "phase" parameter which, unlike pre-ejection period (PEP) or
autonomic nervous system (ANS) surrogate signals, is directly
useful for physiologic rate limiting and control algorithms;
(f) a rate limit/control parameter that reliably responds to pacing
rate before severe hypotension results;
(g) demonstrations of applications which govern pacing rate in
response to these extracted parameters;
(h) a parameter of value for assessing if a tachycardia is
physiologically tolerated for ICD and pacing mode switching
applications; and
(i) a parameter useful for reliably confirming mechanical capture
by pacing when pacing at times substantially different from
anticipated intrinsic times.
Despite the challenges of deriving meaningful physiologic
information from pacing and ICD lead electrodes, the task is worthy
of special effort. The Thevenin equivalent circuit for implantable
electrodes comprises a time varying electrogram source voltage in
series with a time varying impedance element. These fundamental
sources of information are available from standard pacing and
defibrillation leads. Although the "open circuit" electrogram
voltage is almost exclusively a result of myocardial depolarization
and repolarization, impedance depends on electrode and adjacent
tissue geometry and conductivities. The MEP derived parameters of
this invention demonstrate superior robustness to posture and
motion as well as physiologic significance for rate limiting and
control.
A practical implantable device must rely on sensors and extracted
parameters which work well, not just in select cases or conditions.
Instead, the parameters need to be useful in nearly all subjects
and virtually 100% of the time. This follows from constraints that
the life saving device is operational 24 hours a day over a period
of years. An implementation which is not robust to this level can
be expected to exhibit problems which render it substantially
useless. The MEP derived parameters, by delocalizing their
estimation over most or all of the cardiac cycle, offer
independence from nonphysiologic sensor waveform changes.
Furthermore, MEP-phase appears particularly well adapted to
distinguishing physiologically appropriate from inappropriate
tachycardias and physiologically adjusted upper pacing rate
limits.
In summary, MEP is a method of processing which is distributed in
time and consists almost entirely of a series of simple integer
arithmetic and logical operations. MEP computations are compatible
with arbitrary cardiac cycle lengths and derive information from
the first order information available in the sensor signal's
fundamental frequency, amplitude, and phase relative to another
timing signal. The three MEP parameters described below include
MEP-phase (PHA), MEP-magnitude (MAG), and MEP-time (TIM).
These and other features of the present invention will be apparent
to those of skill in the art from a review of the following
detailed description along with the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a overall schematic diagram of a pacemaker wherein the
present invention finds application.
FIG. 2 is a logic flow diagram of the steps of developing an
MEP-parameter signal, such as MEP-phase, from the intracardiac
impedance signal.
FIGS. 3a-3e depict the various waveforms observed in the steps of
FIG. 2.
FIGS. 4a-4d depict a Fourier-style MEP analysis of a segment of
processed impedance, Z.sub.proc, along with values of MEP-magnitude
and MEP-phase above each cycle in FIG. 4a.
FIGS. 5a-5d depict the sensor signal of FIGS. 4a-4d but with square
basis functions, Cosine-Square and Sine-Square and MEP MAG and PHA
results in accordance with this invention.
FIG. 6a is a vector diagram depicting the reliability of an MEP
parameter in which the MAG parameter is sufficiently greater than
non-physiologic noise (.delta..rho.) and FIG. 6b is a plot of an
impedance waveform in such an event.
FIG. 7a is a vector diagram depicting the unreliability of an MEP
parameter in which the MAG parameter is on the same order of
magnitude as non-physiologic noise and FIG. 7b is a plot of an
impedance waveform in such an event.
FIG. 8 is a schematic block diagram of a circuit for the extraction
of MEP parameters.
FIG. 9 is a schematic block diagram of another circuit for the
extraction of MEP parameters.
FIG. 10 depicts a synchronous averaging circuit for ventilatory and
other artifact suppression by specialized low pass filtering.
FIG. 11 depicts a circuit diagram for low pass filtering of the
sensor signal to coerce it into a sinusoid at the fundamental
frequency so that the signal is readily converted into MEP time,
TIM.
FIG. 12 is a series of plots which demonstrate an experimentally
derived response which shows a step response in pacing rate and the
benefits of synchronous averaging and MEP processing in accordance
with this invention.
FIGS. 13a and 13b depict symbolic diagrams which capture the
essential distinction between the physiologic responses of
MEP-phase and PEP, a more conventional time based parameter
indicative of autonomic nervous system activity.
FIG. 14 is a segment of computer code for implementing the MEP
extraction technique depicted in FIG. 8, which derives PHA, TIM,
and MAG from explicit A, B, and T accumulators.
FIG. 15 is a series of plots demonstrating the application of this
invention in detecting and responding to the onset of a potentially
significant tachycardia.
FIG. 16 is a series of plots showing MEP-phase insensitivity to a
postural change of the intracardiac impedance signal at constant
heart rate, and example of an event which is difficult to handle in
known systems.
FIG. 17 is a series of plots demonstrating the usefulness of this
invention in providing a physiologic upper limit on the pacing
rate.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
FIG. 1 depicts a pacemaker 10 in schematic form with connection to
a human heart 12. The present invention may be used for extracting
information from data sensed in the atrium, the ventricle or both;
and both atrial or ventricular pacing or either of them may be
provided.
The pacemaker 10 comprises a microprocessor 14 which executes
various control programs to regulate the action of the pacemaker
10. The microprocessor 14 is connected to a memory 16 for storage
of programs and data as needed.
One or more internal clocks may be provided to permit timing of
various events. For example, an A-V interval timer 18 and a V-A
interval timer 20 may be provided. The microprocessor is also
provided with a telemetry circuit 22 so that communication can be
provided via an antenna 24 to an external programmer (not shown).
Telemetry permits an attending physician to obtain data and
information from the pacemaker and to control the pacemaker to set
various selectable parameters, as well as other functions known in
the art.
The pacemaker 10 is connected to the heart 12 through a first lead
26 to an electrode 27 in the atrium 28 and through a second lead 30
to an electrode 31 in the ventricle 32. An indifferent electrode,
such as a pacemaker can 49, is provided to complete the electrical
circuit through the body. As shown in FIG. 1, the can 49 or outer
casing of the pacemaker serves as the indifferent electrode.
Bipolar leads can also be used with this invention as well as the
unipolar leads illustrated.
Atrial sensing, through an atrial sense circuit 34, and ventricular
sensing, through a ventricular sense circuit 36, provide
information to the microprocessor concerning the condition and
responsiveness of the heart. Also, pacing pulses are provided to
the ventricle from a ventricular stimulus generator 38.
Alternatively, atrial or dual chamber pacing may be provided.
Stimulation of the heart is passed through a coupling capacitor 40
in a conventional fashion.
To control the pulse rate of the ventricular stimulus generator 38,
the microprocessor acquires information on the condition of the
heart through an impedance circuit 42. The impedance circuit 42
detects changes in impedance due primarily to the changing shape of
the heart as it beats and pumps blood. The shape of the impedance
waveform is provided by the impedance circuit 42 to an MEP
extractor 44, as described more fully below. It should be
understood that the present invention is equally applicable to
other time-varying characteristics of the heart.
A sensor 45 may also be provided to obtain an indication of
physiologic need and adjust the pacing rate, as described in U.S.
Pat. No. 5,507,785 and incorporated herein by reference.
The impedance circuit 42 comprises a biphasic signal injector 46
and a signal detector 48. The biphasic signal injector 46 produces
short, essentially symmetrical biphasic constant current pulses to
detect the varying impedance of the heart. Each pulse has a
duration on the order of 1-50 microseconds and an amplitude of
0.1-2 mA. The resulting voltage seen by the detector will be on the
order of 50-1000 mV.
The signal detector 48 is coupled to the lead 30, where it senses
the same signal as that provided to the ventricular sense circuit
36 as a varying impedance signal, or Z.sub.raw. This raw impedance
signal is then provided to the MEP extractor circuit 44, which is
shown in logic flow form in FIG. 2.
The system shown in FIG. 1 demonstrates the present invention in a
two-terminal sensor measuring impedance from the ventricular tip to
the indifferent electrode, which in this case is the can. It should
be understood that this invention is equally applicable to other
two-, three-, and four-terminal sensors as well, and that the
two-terminal sensor is illustrated for clarity, although a
three-terminal sensor may be preferred.
FIG. 2 depicts a logic flow diagram 50 which summarizes the signal
processing steps in carrying out the present invention in the MEP
extractor circuit 44. FIGS. 3a-3e depict signals throughout the
various processing steps of FIG. 2.
Beginning at the tops of FIGS. 2 and 3a-3e, a raw ventricular
impedance sensor signal, Z.sub.raw (FIGS. 2 and 3a), is shown with
active discharge artifacts from dual chamber pacing. An active
discharge artifact is a brief impedance reduction as the residual
polarization is dissipated. The next signal, Z.sub.lpf (FIG. 3b),
has been derived in step 52 from Z.sub.raw by blanking the
artifacts, padding across them, and low pass filtering. Ensemble or
synchronous signal averaging is employed in step 54 to yield the
signal labeled Z.sub.sync (FIG. 3c). Finally, MEP processing yields
physiologic information in the form of the parameter PHA (phase)
56, which is updated once each cardiac cycle as shown in FIG. 3d.
This MEP parameter, shown in FIG. 2 as PHA, is then provided to the
microprocessor 14. Cardiac cycle boundaries are denoted by
V.sub.event pulses (FIG. 3e) which coincide with every ventricular
pace or sense event.
There are a variety of implementations possible to generate MEP
derived information such as PHA (phase), TIM (time), and MAG
(magnitude). In common with all of these implementations is a
process, distributed in time, consisting almost entirely of a
series of simple integer arithmetic and logical operations.
Further, the computation is compatible with arbitrary cardiac cycle
lengths and derives information from the first order information
available in the sensor signal's fundamental frequency, amplitude,
and phase relative to a timing signal available to the pacemaker
(e.g., a pacemaker or electrogram clock).
Basis Function Projections
Before turning to the preferred embodiments of carrying out this
invention as shown in FIGS. 8-11, the following description
provides a background on which this invention is based.
In a manner similar to the Fourier description of periodic signals,
fundamental frequency, magnitude, and phase of the impedance signal
can be obtained from its inner product or projections onto
orthogonal sine and cosine components, as graphically depicted in
FIGS. 4a-4d. Although conceptually ideal, the Fourier weighted sums
depend on trigonometric sine (FIG. 4d) or cosine (FIG. 4c)
functions which in turn depend on the cardiac cycle duration.
Between V.sub.event markers (FIG. 4b), resulting values for each of
four cycles are shown in FIG. 4a for MEP-magnitude (about 3.OMEGA.)
and MEP-phase (about 280.degree.). The computational burden using
this scheme precludes direct integrated circuit implementation in
implantable devices.
Instead, piece-wise constant approximations of sine and cosine
which adapt to cardiac interval variations are used in the
practical implementation in this disclosure. The simplest technique
for such an approximation uses two square waves, 90.degree. out of
phase and thus orthogonal, instead of sine waves. The result, shown
in FIGS. 5a-5d, yields very similar results for magnitude and phase
to those of FIGS. 4a-4d.
As used herein, z.sub.i stands for the i.sup.th impedance signal
sample in a cardiac cycle of length N. The notation Sine-Square
refers to the sinewave-like basis function (FIG. 5d), whose values
are +1 for the first N/2 samples and -1 for the last N/2. For the
embodiments described below, the sine-like square wave requires the
summation over the first half of the cycle, A, subtracted from the
sum over the last half of the cycle. Similarly, the accumulator B
is defined as the sum over the middle half of the cycle.
Mathematically, the accumulator values for A, B, and T are defined:
##EQU1## although these expressions are strictly correct only when
N is a multiple of 4.
Then, the projection or inner product of the sensor signal with the
sine-like and cosine-like basis functions are defined: ##EQU2##
Equations 2 and 3 therefore result in values for X and Y
projections from which the MEP values may be derived. Further, each
of the parameters A, B, and T may be computed in real time with
each impedance sample by a simple algorithm implemented in a
circuit, such as that shown in either of FIGS. 8, 9 or 14.
At the conclusion of a cardiac cycle, the mean impedance (AVG),
MEP-magnitude (MAG), MEP-phase (PHA), and MEP-time (TIM) may be
obtained by trigonometric relations ##EQU3##
where P is the cardiac period and PHA is assumed expressed in
degrees.
Relationships of certain MEP parameters are depicted in FIGS. 6a
and 6b. The region of radius .delta..rho. surrounding the
coordinate location (X,Y) denotes a region of error or uncertainty
jointly in each component which, as long as MAG (of length
.rho.>>.delta..rho.) is relatively large, does not unduly
affect the MEP parameters.
On the other hand, if MAG were comparable to nonphysiologic noise
and measurement errors (.rho..apprxeq..delta..rho.), then MAG and
particularly PHA and TIM information is not reliably determined.
This may occur despite an adequate peak-to-peak impedance signal
level (Z.sub.PP) as shown in FIG. 7b, comparable to that of FIG.
6b.
The MAG, PHA, and TIM results are independent of slowly varying
impedance offsets. This is valuable because physiologic impedance
fluctuations are often a small part of the measured impedance and
offset correction may be used to better exploit the A/D converter's
dynamic range. The implementation of this invention that is
depicted in FIG. 8 requires accessing an array of past (8-bit)
impedance samples. Such an array can also be used by FIR filter
implementations and/or could be part of a synchronous averaging
scheme such as the one described in Equations 5 and 6 and FIG. 10
below.
When using TIM as defined by Equations 1-3 above, Equation 4
amounts to a distributed computation of the time of sensor signal
peak with respect to the onset of the cardiac cycle. This is
superior to a directly determined time to sensor signal maximum for
the various reasons described above. Further, depending on the
coordinate system, this time could just as well refer to the sensor
signal minimum, threshold crossing time such as PEP.sub.50%, or a
variety of other times. For example, one may define an MEP
parameter as follows: MEP-PEP.sub.50% =TIM-(P/4).
Again, the MEP derived parameters are superior to the corresponding
direct determinations by virtue of their dependence on the entire
cardiac cycle's sensor signal.
The MEP Extractor
With this background in mind, now refer to FIG. 8 for a preferred
embodiment of an MEP extractor 44. A raw impedance sensor signal
61, z.sub.raw, is continuously sampled at a fixed rate and fed to a
register 64. The unit 64 also provides blanking and padding of
asynchronously collected impedance signals to prevent pacing pulse
and active discharge interval artifacts from grossly distorting the
impedance waveform. This simple implementation does this by padding
across the blanking interval by holding the output at its last
valid level. Impedance artifacts from atrial and/or ventricular
pacing active discharge intervals are eliminated by the unit 64 by
using a pace indicator 63 to control whether the output z at point
i is the present Z.sub.raw or a past value, z.sub.pad, stored in
the register 64. The duration of the padding interval should be
slightly longer than the discharge intervals. Thus, the unit 64
develops the output signal, z.sub.i.
In the embodiment depicted in FIG. 8, an arithmetic logic unit 62
includes a set of accumulator registers for A, B, and T. The
arithmetic logic unit 62 receives the sampled impedance signal,
z.sub.i, V.sub.event, and a logically ORed input of v.sub.e1 and
v.sub.e2. v.sub.e1 is the microprocessor signal to the ventricular
stimulus generator 38 and v.sub.e2 is the signal from the
ventricular sense circuit 36 (see FIG. 1).
The arithmetic logic unit 62 provides the means for MEP processing.
The blanked sensor signal z is input to the ALU 62 together with a
V.sub.event indicator to signal the end of one cardiac cycle and
the beginning of the next. An array of sensor signal samples is
written to and read from using addresses pa, pb, pc, and i to
continuously revise the A, B, and T results in the ALU 62. Upon
signaling the end of a cardiac cycle, these results are transferred
into a set of registers 66 to be held for the microprocessor 14 to
transform into mean, MAG, TIM, and PHA that are used in rate
control or rate limiting algorithms.
A sample segment of code for the calculations just described is
shown in FIG. 14. Each sample of the sensor signal is read in
serially, and the values for the various registers are calculated
in real time. Note particularly the technique that is used for
corrections for various values for N when N is not a multiple of
4.
FIG. 9 depicts an MEP extractor as an alternative to that shown in
FIG. 8 if memory storage is cheap. The MEP extractor 44 may store
the array of partial sums ##EQU4## in a memory array 74. However,
the partial sum array elements Z.sub.i need to be at least 16-bits
requiring at least twice as much storage as in the embodiment of
FIG. 8. In the case of FIG. 9, the sum for the first half
A=Z.sub.N/2 and the total T=Z.sub.N are read directly from the
array 74 (although interpolation is needed to compute A when N is
odd). Similarly, the sum over the middle half is B=Z.sub.3N/4
-Z.sub.N/4 (again interpolation is required when N is not a
multiple of 4). The system of FIG. 9 is preferable to that of FIG.
8 if alternative piece-wise constant basis functions are desired
and if the basis functions are to be flexibly determined by
software.
Referring again to FIG. 9 and beginning with a blanked sensor
signal z at the bottom left which may be developed in manner
similar to that provided by unit 64 in FIG. 8, a low pass filtered
version, z.sub.fir, is produced by a simple boxcar type FIR filter
76. For each sample, the accumulator A is increased by z.sub.i and
decreased by Z.sub.i-G (after A has been initialized correctly) and
then Z.sub.fir =A/G. Optionally, this low pass filtering step may
be omitted.
The MEP computation of A, B, and T is performed by the ALU 72. For
each new signal sample, z.sub.fir, the cumulative sum variable Z is
increased and stored in an array 74. Upon the V.sub.event indicator
asserting that the cardiac cycle consisting of N events has just
ended, up to 6 values are read from this array; interpolation is
performed to yield the cumulative sums at N/4, N/2, and 3N/4; and
then A, B, and T are computed and stored in a set of registers 78.
As before, the values A, B, and T are then provided to the
microprocessor for the development of the MEP extraction
parameters.
Synchronous Averaging
FIG. 10 depicts a circuit 80 for specialized low pass filtering and
ventilatory artifact suppression known as synchronous averaging.
The output of the circuit is immediately compatible with MEP
extraction described above. The synchronous averaging circuit 80 is
useful in regularizing and cleaning cardiac cycle fluctuations by
suppressing perturbations peculiar to specific cycles. The
parameters derived from such signals are thus inherently low pass
filtered and the resulting improved parameter stability has led to
more robust rate limiting and control applications. For some
extremely "noisy" sensor signals, such as the top signal of FIG.
12, the result is a dramatic improvement for the synchronous
averaged signal.
The V.sub.event indicator, fed to an ALU 82, initializes an array
index i at the start of each cardiac cycle. For each new signal
sample within a cardiac cycle k, the prior synchronously averaged
output at index i (from cycle k-1) is blended with z.sub.i in a
convex combination to make a 1-pole IIR low pass filter: ##EQU5##
Alternatively, an FIR low pass filter blend may be used but
requires additional memory storage for past cardiac cycles.
The values thus derived are stored in an array 84. If the present
index extends beyond the end of the last cardiac cycle, N.sup.k-1,
the circuit 80 depends on only the present sensor signal in the
present beat, z.sub.i.sup.k. The terms (zs.sub.N.spsb.k-1.sup.k
-z.sub.N.spsb.k-1.sup.k) and (-z.sub.i-1.sup.k -zs.sub.i-1.sup.k)
are offset corrections to splice cleanly with the synchronously
averaged signal. In practice, one should choose .alpha.=p/m and m
to be a power of 2. A good choice for flexibility and function is
m=8 and p in the range of 4 through 6.
Ventilation and Baseline Drift Removal Extension to MEP
Calculations
Ventilation related variations of the impedance baseline over a
single cardiac cycle induce small errors in the MEP parameters. The
MEP extraction methods and structure previously described both
permit simple linear "detrending" corrections. For example, if a
cardiac cycle of N sensor samples ended d=z.sub.N -z.sub.1 apart,
one may correct the A, B, and T accumulators by ##EQU6## Low Pass
Filter and Comparator
FIG. 11 depicts an alternative, although not the preferred,
implementation of this invention which relies on IIR or FIR low
pass filtering of the sensor signal to coerce it into a sinusoid at
the fundamental frequency. Once this is done, any fiducial point of
the resulting sinusoid (such as time-to-max or better, or the time
to 50% of the way down from max to min) is readily converted into
MEP phase or time as previously described. However, this approach
has certain drawbacks:
(a) the original signal's magnitude is reduced by the strong low
pass filter attenuation to a variable extent dependent on the heart
rate;
(b) this embodiment requires a multirate or large order FIR filter
or an IIR filter with high precision internal states to achieve the
strong low pass filtering required for sufficient accuracy;
(c) ventilation and baseline drift compensation as described in
Equation 7 above are not possible;
(d) the reduced amplitude of the signal makes the determination of
time and phase angle less accurate;
(e) the time delay introduced by causal low pass filters generates
an artificial coupling of heart rate to PHA and TIM which is
particularly objectionable at high rates and requires
compensation;
(f) this embodiment cannot capitalize on the benefits of coherent
or synchronous signal averaging such as given in Equations 5 and 6;
and
(g) this embodiment cannot decrease the sampling rate to below the
resolution needed for direct determination of times.
FIG. 11 depicts a diagram for implementing this method of heavy low
pass filtering and slope/level triggering. The result is i* the
last time during cardiac cycle k that (for example)
zlpf.sub.i.sup.k >Th.sup.k-1 and Zlpf.sub.i-1.sup.k
.ltoreq.Th.sup.k-1, where threshold Th.sup.k-1 is the mean of
z.sub.lpf.sbsb.i over cardiac cycle k-1. If the phase delay
associated with the low pass filter for cardiac frequencies is c
samples, then under these assumptions
PHA=360.degree..times.((i*-c)/N.sup.k +1/4).
The regularly sampled, blanked, and padded impedance signal may be
filtered by cascading two 4-pole IIR filters, each of which operate
with extended precision multiply and accumulates due to the
disparity between sampling rate and cutoff frequency, in an ALU 92.
Alternatively, multirate FIR filters may be cascaded. The low pass
filtered signal, reduced to its cardiac fundamental frequency
component, is used as input to a slope and level comparator 94. The
resulting i* is stored in a register 96 for transfer to the
microprocessor.
Causal Filter Time Delay Compensation Extension
If causal filtering is performed on the sensor signal, it will
delay this signal with respect to the clock or electrogram timing
reference. The filter's time delay may be defined as .DELTA.
seconds or c samples. This delay will provide an additional but
nonphysiologic phase to cycle k's MEP-phase, PHA.sup.k. The extent
of this increase depends on the duration of that cycle, D.sup.k
seconds or N.sup.k samples. This artificial relationship of
MEP-phase to rate may be removed by ##EQU7## If the relative
contribution to phase is small, say less than 20.degree. at the
higher heart rates, then this correction may be neglected.
Experience has shown, however, that this correction is necessary
when applying significant low pass filtering to cardiac impedance
signals.
The distributed nature of computations in MEP derived parameter
extraction gives rise to significant advantages in the minimum
sensor sampling frequency and sampling resolution required.
Experience has shown that reducing the impedance sampling frequency
has not degraded the performance of this invention. Rather, the MEP
extraction parameters that result from the lower sampling frequency
are essentially identical to those using a high sampling frequency.
The same result has been found for a lower signal level resolution.
A reduction of either sampling frequency or analog to digital
resolution translates into less electrical current required to make
and process each sensor sample. These benefits allow smaller and
longer lasting implantable devices.
The discussion above has defined MEP's distributed computations and
has illustrated how the derived parameters can be used in an
implantable cardiac device as shown in FIG. 1. MEP parameters,
unlike specific time parameters such as pre-ejection period (PEP),
are not exclusively linked to specific components of the cardiac
cycle, such as isovolumic contraction. Rather, they are an
integrated property of anatomic and physiologic actions over a
cardiac cycle.
As a result of distributing its computations over the cardiac
cycle, MEP is well suited to assess if there is sufficient time for
the entire sequence of systolic and diastolic events to take place
or if the cardiac cycle time is too short. If one fixes the level
of exertion and autonomic nervous system stimulation, the time
required for systole (e.g. TIM) is observed to be fairly constant.
Inappropriate increases in heart rate result in diastole becoming
too short to adequately fill the heart for subsequent contractions.
The cardiac cycle's duration when at the verge of inadequate
diastole is referred to by Spinelli as Total Active Time or
TAT.
The reduction of cardiac interval during exertion, an appropriate
physiologic tachycardia, is accompanied by similar reductions of
both systole, TIM, and diastole by virtue of the sympathetic part
of the autonomic nervous system. On the other hand, pathologic
tachycardias of either paced or intrinsic origin are not
accompanied by ANS shortening of systole or TIM causing an
abbreviated diastole to occur late in the cardiac cycle. The
mechanical, elastic, inertial, intracardiac volume, and wall
tension changes associated with systolic contraction and ejection
thus appear relatively later in the cardiac cycle. As a result,
MEP-phase of the impedance signal, which depends on all of these,
is increased.
An example of a strong contribution of synchronous averaging to
sensor signal processing is shown in FIG. 12. The actual impedance
signal, after blanking and padding for pacing artifacts, appears in
the top panel, Z.sub.proc. At the lower pacing rate of 80 bpm,
Z.sub.proc is fairly classical in shape but alters into a flat
topped signal after the rate increases to 100 bpm. The
synchronously averaged sensor signal, Z.sub.sync, in the second
panel, was derived from Equation 5 above and provides a responsive
rendering of consistent changes to the sensor waveform. A clear
rate-associated parameter change is seen in MEP-phase but is better
defined when derived from Z.sub.sync (PHA.sub.sync) instead of
Z.sub.proc (PHA.sub.proc). Note the almost step-like rise from
135.degree. to 175.degree. in the panel labeled PHA.sub.sync.
Parameters of this quality are necessary for stable rate limit and
control algorithms.
A pair of symbolic diagrams (FIGS. 13a and 13b) show the essential
distinction between the physiologic responses of MEP-phase and PEP,
a more conventional time based parameter. As described above, PHA
indicates directly a discrepancy between appropriate and
inappropriate rates. By comparison, PEP and TIM reflect the
autonomic nervous system's activity and physiologic demand.
FIG. 13a is a diagram of the response of MEP phase (PHA) to two
hypothetical conditions under which heart rate may be increased.
Under the condition of exercise and sympathetic autonomic nervous
system (ANS) stimulation, both systole and diastole shorten,
usually to a similar degree. As a result, although the frequency
increases, the phase of the signal remains nearly constant at its
normal baseline level. In contrast, pacing in the absence of
metabolic demand (as well as pathologic tachycardias) creates a
situation in which PHA increases since diastole is primarily
shortened. One of the assets of PHA is that it directly indicates a
discrepancy between appropriate and inappropriate rates in a
variety of circumstances. It may thus be used directly in rate
limiting and control algorithms to direct changes in therapeutic
stimulation when, for example, it exceeds a threshold level.
In FIG. 13b, a conventional autonomic nervous system responsive
variable such as PEP is shown to help compare with PHA in FIG. 13a.
PEP (even when robustly resolved) reflects the ANS activity and
demand. As such, it is useful in algorithms as an activity sensor
surrogate or check, but is not immediately helpful in
moment-to-moment pacing rate determination, since when exertion is
fixed and pacing rate changes, PEP does not change significantly in
response. The fact that PEP is greater or less than some threshold
does not necessarily means that the pacing rate is inappropriately
fast or slow.
Coordinate System Issues
There is an arbitrariness to the MEP-phase coordinate system
regarding the order and sign of the arguments to the atan2()
function of Equation 4. A preferred coordinate system usually
assigns a positive angular values of 90.degree. to 120.degree.
under normal sinus rhythm and normal low rates. This coincides with
an impedance signal peak about 1/4 to 1/3 of the way through the
cardiac cycle. Under excessively high paced rates or pathologic
tachycardias, the values may exceed 300.degree.. Occasionally, in
these same circumstances, the PHA values may exceed 360.degree.
when the impedance peak occurs later than the next ventricular
paced or sensed event. Since phase is circular and wraps at
360.degree., it may be unwrapped to extend to values greater than
360.degree. by any of a variety of methods such as past history or
rate criteria.
Comparisons or computations based on MEP-phase need to be
implemented either with the unwrapped value or interpreted modulo
2.pi. or 360.degree. to avoid radically different responses. As an
example, assume that the rate algorithm's threshold or setpoint is
320.degree. and that the measured phase is from 358.degree. to
2.degree.. We must respond to any phase in that interval with a
response appropriate to a discrepancy which is high by
38-42.degree., and not one which is low by about
318.degree.-320.degree..
With this in mind, refer now to FIG. 15. FIG. 15 depicts a series
of traces for sudden onset of pacing simulated ventricular
tachycardia at 150 bpm from a sinus rhythm baseline of 80 bpm. In
this case, the moderate rate tachycardia is physiologically
tolerated, judging from the traces for arterial blood pressure,
ABP, and pulmonary artery pressure, PAP. FIG. 15 also shows
MEP-TIM, MEP-MAG, and MEP-PHA. PHA increases dramatically from
about 125.degree. to about 220.degree.. In contrast, PEP shows a
paradoxical rise but TIM reveals a small decrease in the direction
expected by ANS response. The fact that PHA remains just over
200.degree. suggests, independent of pressure or flow, that there
is just sufficient time in the cardiac cycle for diastole. This
information is useful to determine that therapy might be withheld,
at least initially, and thus serves to discriminate tolerable
tachycardias.
This information is also useful in the event of an intolerable
tachycardia. By setting predetermined levels for MEP parameters,
particularly MEP PHA, and comparing the predetermined phase with
the phase of the impedance signal, then the system of this
invention may initiate antitachycardia therapies including burst
pacing or defibrillation shocks. The system may also be adapted to
compare the predetermined phase with the phase of the impedance or
other sensor signal while pacing at a rate or timing distinct from
the intrinsic rate or a safety pacing pulse to assess pacing
capture and adjust the pacing output pulse amplitude
accordingly.
FIG. 16 depicts yet another advantageous feature of this invention.
In this case, the subject changed posture from sitting to lying on
his side, and the MEP-phase remained insensitive to this change in
posture. Note that the peak-to-peak impedance signal, Z.sub.pp, and
MEP-MAG parameter nearly doubles in value, and that PEP is
unreliable, but the PHA parameter remains almost constant at
300.degree..
Finally, the present invention is also useful in governing the
upper pacing rate, as shown in FIG. 17. This figure depicts an
example of a pacing rate upper limit governed by MEP-phase. At a
fixed workload or level of autonomic nervous system activity, PHA
is closely tied to the actual pacing rate (compare pace rate with
sensor signal in FIG. 17). In this case, a desired pacing rate
trajectory, which extends from 100 to 300 bpm is dynamically
limited to about 130 bpm and 180 bpm as a result of phase limits
set at 248.degree. and 301.degree., respectively. Arterial blood
pressure is not seriously affected until the MEP-phase exceeds
280.degree.. Note the effect on blood pressure at about 180 seconds
with MEP-phase limit set at 301.degree.. Thus, the quality of this
parameter permits simple rate limiting algorithms which can
dynamically readjust the upper limit as the subject
accommodates.
Basis Function Extensions
An extension to better piecewise constant approximations to sine
and cosine basis functions can be made either by extra accumulators
with additional logic to catch just the regions near the sine and
cosine peaks, or from the array of partial sums with subtractions
again. More ideal versions of sine and cosine are preferred if the
sensor signal contains high harmonics and sharp edges. However,
since the squarewave sine and cosine consist only of odd harmonics
weighted by 1/f, there is likely to be little benefit extending
this past one additional level of approximation. The technique
described above with regard to FIG. 9 is most flexible with regard
to basis function implementations. Harmonics other than the
fundamental may also be sought in this manner from a sensor signal
by using basis functions which repeat over the cardiac cycle.
Furthermore, nonsinusoidal basis functions may be employed to
capitalize on information in a specific sensor signal.
The principles, preferred embodiment, and mode of operation of the
present invention have been described in the foregoing
specification. This invention is not to be construed as limited to
the particular forms disclosed, since these are regarded as
illustrative rather than restrictive. Moreover, variations and
changes may be made by those skilled in the art without departing
from the spirit of the invention.
* * * * *